This paper reviews the main studies on transit users’ route choice in the context of transit assignment. The studies are categorized into three groups: static transit assignment, within‐day dynamic transit assignment, and emerging approaches. The motivations and behavioural assumptions of these approaches are re‐examined. The first group includes shortest‐path heuristics in all‐or‐nothing assignment, random utility maximization route‐choice models in stochastic assignment, and user equilibrium based assignment. The second group covers within‐day dynamics in transit users’ route choice, transit network formulations, and dynamic transit assignment. The third group introduces the emerging studies on behavioural complexities, day‐to‐day dynamics, and real‐time dynamics in transit users’ route choice. Future research directions are also discussed.

Destination familiarity is thought to critically influence tourists’ decision-making processes. Yet the role of familiarity in shaping tourists’ and residents’ image of, and loyalty to, a destination remains uncertain. This research tests a complex and holistic model of familiarity, affective, cognitive and overall images, and the conative behavioural intentions of visiting and recommending the destination for both residents and visitors in the context of the emerging tourism destination of Molise, Italy. The results reveal that residents and visitors differ in terms of their familiarity and intention to visit a place, with familiarity being less likely to influence residents’ intentions. There is heterogeneity between residents and visitors’ affective image and intention to visit, as well as between their overall image and intention to recommend Molise. Hence, unlike visitors, residents are more likely to respond to factual cognitive imaging, rather than emotional messaging, suggesting that shifting residents’ perceptions of place image requires a different approach to that of visitors. Future research should seek to confirm the relationships in a multi-destination study.

Critical success factors (CSFs) have been around since the late 1970s and have been used extensively in information systems implementations. CSFs provide a comprehensive understanding of the multiple layers and dimensions of implementation success. In the specific context of learning analytics (LA), identifying CSFs can maximise the possibilities of an effective implementation and harness the value of converting data into actionable information. This paper proposes a framework that aims to identify and explore the CSFs for the implementation of LA within the higher education sector by examining the viewpoints of higher education professionals. To obtain a rounded insight into stakeholders’ perceptions, we conducted a mixed-method inquiry with factor analysis, profile analysis and thematic analysis of both quantitative and qualitative data collected with an online questionnaire from an international sample. The responses validate five CSFs of LA implementation: strategy and policy at organisational level, information technological readiness, performance and impact evaluation, people’s skills and expertise and data quality. Results also disclose diverse views about the CSFs’ priorities and the associated difficulties and achievements. Implications for practice or policy: Higher education practitioners should consider CSFs for implementing an LA initiative successfully. This study validates five dimensions of the CSFs of implementing LA in higher education. The validated framework enumerates several factors in each of the main dimensions for achieving optimum results. Stakeholders have diverse opinions about the priorities of CSFs, particularly in organisational commitment, data quality and human capital.